

'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class 					_snake_case		:
			__A  :	Dict        =BlenderbotConfig
			__A  :	Union[str, Any]        ={}
			__A  :	Any        ="gelu"






			def __init__(  self			,_snake_case			,_snake_case=13			,_snake_case=7			,_snake_case=True			,_snake_case=False			,_snake_case=99			,_snake_case=32			,_snake_case=2			,_snake_case=4			,_snake_case=37			,_snake_case=0.1			,_snake_case=0.1			,_snake_case=20			,_snake_case=2			,_snake_case=1			,_snake_case=0			,):
				UpperCAmelCase_     :      List[Any]						=  parent
				UpperCAmelCase_     :      str						=  batch_size
				UpperCAmelCase_     :      Dict						=  seq_length
				UpperCAmelCase_     :      int						=  is_training
				UpperCAmelCase_     :      Optional[Any]						=  use_labels
				UpperCAmelCase_     :      Any						=  vocab_size
				UpperCAmelCase_     :      Optional[int]						=  hidden_size
				UpperCAmelCase_     :      Optional[int]						=  num_hidden_layers
				UpperCAmelCase_     :      int						=  num_attention_heads
				UpperCAmelCase_     :      Tuple						=  intermediate_size

				UpperCAmelCase_     :      Any						=  hidden_dropout_prob
				UpperCAmelCase_     :      Optional[int]						=  attention_probs_dropout_prob
				UpperCAmelCase_     :      List[Any]						=  max_position_embeddings
				UpperCAmelCase_     :      str						=  eos_token_id
				UpperCAmelCase_     :      List[Any]						=  pad_token_id
				UpperCAmelCase_     :      List[Any]						=  bos_token_id






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Any						=  ids_tensor([self.batch_size, self.seq_length - 1]			,self.vocab_size     )
				UpperCAmelCase_     :      int						=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )			,1     )
				UpperCAmelCase_     :      Optional[Any]						=  tf.concat([input_ids, eos_tensor]			,axis=1     )

				UpperCAmelCase_     :      int						=  ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size     )

				UpperCAmelCase_     :      Optional[Any]						=  self.config_cls(
				    vocab_size=self.vocab_size			,d_model=self.hidden_size			,encoder_layers=self.num_hidden_layers			,decoder_layers=self.num_hidden_layers			,encoder_attention_heads=self.num_attention_heads			,decoder_attention_heads=self.num_attention_heads			,encoder_ffn_dim=self.intermediate_size			,decoder_ffn_dim=self.intermediate_size			,dropout=self.hidden_dropout_prob			,attention_dropout=self.attention_probs_dropout_prob			,max_position_embeddings=self.max_position_embeddings			,eos_token_ids=[2]			,bos_token_id=self.bos_token_id			,pad_token_id=self.pad_token_id			,decoder_start_token_id=self.pad_token_id			,**self.config_updates			,)
				UpperCAmelCase_     :      List[str]						=  prepare_blenderbot_inputs_dict(_snake_case			,_snake_case			,_snake_case     )
				return config, inputs_dict






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  TFBlenderbotModel(config=_snake_case     ).get_decoder()
				UpperCAmelCase_     :      int						=  inputs_dict["input_ids"]

				UpperCAmelCase_     :      Dict						=  input_ids[:1, :]
				UpperCAmelCase_     :      Any						=  inputs_dict["attention_mask"][:1, :]
				UpperCAmelCase_     :      int						=  inputs_dict["head_mask"]
				UpperCAmelCase_     :      Optional[int]						=  1

				# first forward pass
				UpperCAmelCase_     :      List[str]						=  model(_snake_case			,attention_mask=_snake_case			,head_mask=_snake_case			,use_cache=_snake_case     )

				UpperCAmelCase_					,					UpperCAmelCase_     :      List[Any]						=  outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				UpperCAmelCase_     :      Optional[int]						=  ids_tensor((self.batch_size, 3)			,config.vocab_size     )
				UpperCAmelCase_     :      Any						=  tf.cast(ids_tensor((self.batch_size, 3)			,2     )			,tf.inta     )

				# append to next input_ids and
				UpperCAmelCase_     :      Union[str, Any]						=  tf.concat([input_ids, next_tokens]			,axis=-1     )
				UpperCAmelCase_     :      Any						=  tf.concat([attention_mask, next_attn_mask]			,axis=-1     )

				UpperCAmelCase_     :      Any						=  model(_snake_case			,attention_mask=_snake_case     )[0]
				UpperCAmelCase_     :      List[Any]						=  model(_snake_case			,attention_mask=_snake_case			,past_key_values=_snake_case     )[0]

				self.parent.assertEqual(next_tokens.shape[1]			,output_from_past.shape[1]     )

				# select random slice
				UpperCAmelCase_     :      str						=  int(ids_tensor((1,)			,output_from_past.shape[-1]     )     )
				UpperCAmelCase_     :      List[str]						=  output_from_no_past[:, -3:, random_slice_idx]
				UpperCAmelCase_     :      Union[str, Any]						=  output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(_snake_case			,_snake_case			,rtol=1E-3     )




def  a__       (    _SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       str=None	,				_SCREAMING_SNAKE_CASE   :       Any=None	,				_SCREAMING_SNAKE_CASE   :       Any=None	,				_SCREAMING_SNAKE_CASE   :       List[str]=None	,				_SCREAMING_SNAKE_CASE   :       Dict=None	,				)     ->					Union[str, Any]:


	"""simple docstring"""





	if attention_mask is None:
		UpperCAmelCase_     :      Dict						=  tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE	,				config.pad_token_id				)	,				tf.inta				)
	if decoder_attention_mask is None:
		UpperCAmelCase_     :      Optional[int]						=  tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape	,				dtype=tf.inta				),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,				config.pad_token_id				)	,				tf.inta				),
		    ]	,				axis=-1	,				)
	if head_mask is None:
		UpperCAmelCase_     :      List[Any]						=  tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
	if decoder_head_mask is None:
		UpperCAmelCase_     :      Optional[int]						=  tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
	if cross_attn_head_mask is None:
		UpperCAmelCase_     :      str						=  tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}
@require_tf
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Union[str, Any]        =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
			__A  :	List[str]        =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
			__A  :	Dict        =(
			    {
			        "conversational": TFBlenderbotForConditionalGeneration,
			        "feature-extraction": TFBlenderbotModel,
			        "summarization": TFBlenderbotForConditionalGeneration,
			        "text2text-generation": TFBlenderbotForConditionalGeneration,
			        "translation": TFBlenderbotForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__A  :	Any        =True
			__A  :	Dict        =False
			__A  :	Dict        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  TFBlenderbotModelTester(self     )
				UpperCAmelCase_     :      int						=  ConfigTester(self			,config_class=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				self.config_tester.run_common_tests()






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_snake_case     )




@require_tokenizers
@require_tf
class 					_snake_case		(unittest.TestCase):
			__A  :	Optional[int]        =["My friends are cool but they eat too many carbs."]
			__A  :	Optional[Any]        ="facebook/blenderbot-400M-distill"






			@cached_property
			def 		UpperCamelCase__							(  self     ):
				return BlenderbotTokenizer.from_pretrained(self.model_name     )






			@cached_property
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
				return model






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.tokenizer(self.src_text			,return_tensors="tf"     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.model.generate(
				    model_inputs.input_ids			,)
				UpperCAmelCase_     :      str						=  self.tokenizer.batch_decode(generated_ids.numpy()			,skip_special_tokens=_snake_case     )[0]
				assert (
				    generated_words
				    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
				)










"""simple docstring"""




import random



def        a__					(							snake_case__			,					snake_case__			,					snake_case__ = False							)		->   dict:
			lowerCamelCase      =   {i: [] for i in range(snake_case__							)}

			# if probability is greater or equal than 1, then generate a complete graph
			if probability >= 1:
						return complete_graph(snake_case__							)
			# if probability is lower or equal than 0, then return a graph without edges
			if probability <= 0:
						return graph

			# for each couple of nodes, add an edge from u to v
			# if the number randomly generated is greater than probability probability
			for i in range(snake_case__							):
						for j in range(i + 1			,					snake_case__							):
									if random.random() < probability:
												graph[i].append(snake_case__							)
												if not directed:
															# if the graph is undirected, add an edge in from j to i, either
															graph[j].append(snake_case__							)
			return graph



def        a__					(							snake_case__							)		->   dict:
			return {
			    i: [j for j in range(snake_case__							) if i != j] for i in range(snake_case__							)
			}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



